Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Understanding Machine Learning
20141.7k citationsShai Shalev‐Shwartz et al.profile →
Understanding Machine Learning: From Theory To Algorithms
20151.5k citationsShai Shalev‐Shwartz et al.profile →
Countries citing papers authored by Shai Shalev‐Shwartz
Since
Specialization
Citations
This map shows the geographic impact of Shai Shalev‐Shwartz's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Shai Shalev‐Shwartz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shai Shalev‐Shwartz more than expected).
Fields of papers citing papers by Shai Shalev‐Shwartz
This network shows the impact of papers produced by Shai Shalev‐Shwartz. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Shai Shalev‐Shwartz. The network helps show where Shai Shalev‐Shwartz may publish in the future.
Co-authorship network of co-authors of Shai Shalev‐Shwartz
This figure shows the co-authorship network connecting the top 25 collaborators of Shai Shalev‐Shwartz.
A scholar is included among the top collaborators of Shai Shalev‐Shwartz based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Shai Shalev‐Shwartz. Shai Shalev‐Shwartz is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Shalev‐Shwartz, Shai, et al.. (2018). Average Stability is Invariant to Data Preconditioning. Implications to Exp-concave Empirical Risk Minimization. Journal of Machine Learning Research. 18(222). 1–13.13 indexed citations
2.
Malach, Eran & Shai Shalev‐Shwartz. (2017). Decoupling "when to update" from "how to update". Neural Information Processing Systems. 30. 960–970.88 indexed citations
3.
Shalev‐Shwartz, Shai, et al.. (2017). Fast Rates for Empirical Risk Minimization of Strict Saddle Problems. Conference on Learning Theory. 1043–1063.3 indexed citations
4.
Globerson, Amir, Roi Livni, & Shai Shalev‐Shwartz. (2017). Effective Semisupervised Learning on Manifolds. Conference on Learning Theory. 978–1003.3 indexed citations
5.
Hazan, Elad, Kfir Y. Levy, & Shai Shalev‐Shwartz. (2016). On graduated optimization for stochastic non-convex problems. International Conference on Machine Learning. 1833–1841.10 indexed citations
6.
Daniely, Amit & Shai Shalev‐Shwartz. (2016). Complexity Theoretic Limitations on Learning DNF’s. Conference on Learning Theory. 815–830.6 indexed citations
7.
Vinnikov, Alon & Shai Shalev‐Shwartz. (2014). K-means recovers ICA filters when independent components are sparse. International Conference on Machine Learning. 712–720.12 indexed citations
8.
Shamir, Ohad & Shai Shalev‐Shwartz. (2014). Matrix completion with the trace norm: learning, bounding, and transducing. Journal of Machine Learning Research. 15(1). 3401–3423.16 indexed citations
9.
Cotter, Andrew, Shai Shalev‐Shwartz, & Nati Srebro. (2013). Learning Optimally Sparse Support Vector Machines. International Conference on Machine Learning. 266–274.24 indexed citations
Sabato, Sivan & Shai Shalev‐Shwartz. (2008). Ranking Categorical Features Using Generalization Properties. Journal of Machine Learning Research. 9(37). 1083–1114.5 indexed citations
17.
Shalev‐Shwartz, Shai & Yoram Singer. (2008). On the Equivalence of Weak Learnability and Linear Separability: New Relaxations and Efficient Boosting Algorithms.. Conference on Learning Theory. 311–322.6 indexed citations
18.
Sridharan, Karthik, Shai Shalev‐Shwartz, & Nathan Srebro. (2008). Fast Rates for Regularized Objectives. Neural Information Processing Systems. 21. 1545–1552.53 indexed citations
19.
Shalev‐Shwartz, Shai & Sham M. Kakade. (2008). Mind the Duality Gap: Logarithmic regret algorithms for online optimization. Neural Information Processing Systems. 21. 1457–1464.41 indexed citations
20.
Dekel, Ofer, Shai Shalev‐Shwartz, & Yoram Singer. (2004). The Power of Selective Memory: Self-Bounded Learning of Prediction Suffix Trees. Neural Information Processing Systems. 17. 345–352.11 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.